Today, the demand on higher speed of Wi-Fi is driven by the need to support more applications at higher quality. The Wi-Fi protocol has been constantly updated to meet such demands with newer technologies, such as adopting Orthogonal Frequency Division Multiplexing (OFDM) since 802.11a, Multiple-Input-Multiple-Output (MIMO) since 802.11n, and Multi-User MIMO (MU-MIMO) with the latest 802.11ac. One of the major barriers to fully exploiting new technologies such as MU-MIMO, or beamforming with single user MIMO, is the high overhead of the Channel State Information (CSI) feedback. The CSI for an antenna pair is a vector of complex numbers representing the channel coefficients of the OFDM subcarriers, and is the key to calculating the modulation parameters for the data transmission. In Wi-Fi, the CSI is typically measured at the receiver and is transmitted back to the sender, which requires significant overhead. For example, the full CSI for a single antenna pair on a 20 MHz channel has 64 complex numbers; if there are 9 antenna pairs, the full CSI has 9 vectors with 576 complex numbers which may exceed 1000 bytes.
The Wi-Fi standard defines options to compress the CSI, such as reducing the quantization accuracy or the number of subcarriers in the feedback, or using the Given's rotation on the V matrix after the Singular Value Decomposition (SVD) of the CSI matrix. However, these methods either reduce the accuracy of the CSI, or only achieve modest compression ratios. For example, a 3 by 3 complex V matrix can only be compressed into 6 real numbers, at a compression ratio of 3. The need for CSI feedback reduction in wireless networks in general is well known in the art.
As such, while there have been many attempts to compress the CSI to reduce the overhead, such as reducing the quantization accuracy or the number of subcarriers in the feedback, or using the sinusoidal representation of the CSI, the existing solutions either sacrifice accuracy or exhibit high computational complexity.
Accordingly, what is needed in the art is an improved system and method for compressing the CSI for OFDM that is accurate and computationally easy to implement.